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Technical Paper

A Fuzzy System for Automotive Fault Diagnosis

1998-02-23
981074
This paper describes a fuzzy model that is designed to diagnose automotive engineering faults. The fuzzy model has two modes, L-mode, which is the fuzzy learning mode and T-mode, which is the test mode. In the L-mode, the system learns two types of engineering diagnostic knowledge, expert knowledge, and the knowledge acquired from training data using machine learning techniques. A fuzzy diagnostic system for engine vacuum leak detection has been implemented based on this fuzzy model. The system has been tested on the data downloaded directly from the test sites of assembly plants of the Ford Motor Company, and its performance is excellent.
Technical Paper

Precise Real-Time Factory Diagnostics Via Machine Learning - The RADE Algorithm

1998-05-12
981338
RADE (the Rockwell Automation Diagnostic Engine) provides accurate, sensitive detection and reporting of faults in automated factory machinery. It uses machine learning to learn the normal behavior of the particular machine being monitored. It then reports any deviations from normal behavior.
Technical Paper

Naturalistic Driving Behavior Analysis under Typical Normal Cut-In Scenarios

2019-04-02
2019-01-0124
Cut-in scenarios are common and of potential risk in China but Advanced Driver Assistant System (ADAS) doesn’t work well under such scenarios. In order to improve the acceptance of ADAS, its reactions to Cut-in scenarios should meet driver’s driving habits and expectancy. Brake is considered as an express of risk and brake tendency in normal Cut-in situations needs more investigation. Under critical Cut-in scenarios, driver tends to brake hard to eliminate collision risk when cutting in vehicle right crossing lane. However, under less critical Cut-in scenarios, namely normal Cut-in scenarios, driver brakes in some cases and takes no brake maneuver in others. The time when driver initiated to brake was defined as key time. If driver had no brake maneuver, the time when cutting-in vehicle right crossed lane was defined as key time. This paper focuses on driver’s brake tendency at key time under normal Cut-in situations.
Technical Paper

Intelligent Vehicle Monitoring for Safety and Security

2019-04-02
2019-01-0129
The challenges posed by connected and autonomous vehicles fall beyond the scope of current version of ISO 26262. According to the current functional safety standard, controllability, largely affected by human intervention, is a large contributor to the definition of the Automotive Safety Integrity Level (ASIL). Since the driver involvement in CAVs will decrease in future, this gives no clear definition for future functional safety design. On the other hand, CAVs bring additional capabilities such as advance sensors, telematics-based connectivity etc. which can be used to devise efficient approaches to address functional safety (FuSa) challenges. The caveat to these additional capabilities is issues like cybersecurity, complexity, etc.
Technical Paper

Vehicle Velocity Prediction and Energy Management Strategy Part 1: Deterministic and Stochastic Vehicle Velocity Prediction Using Machine Learning

2019-04-02
2019-01-1051
There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.
Journal Article

Vibration Rating Prediction Using Machine Learning in a Dynamic Skip Fire Engine

2019-04-02
2019-01-1054
Engines equipped with Dynamic Skip Fire (DSF) technology generate low frequency and high amplitude excitations that could reduce vehicles drive quality if not properly calibrated. The excitation frequency of each firing pattern depends on its length and on the rotational speed of the engine. Excitation amplitude mainly depends on the requested engine torque by the driver. During the calibration process, the torque characteristics that results in production level of noise, vibration, and harshness (NVH), must be identified, for each firing pattern and engine speed. This process is quite time consuming but necessary. To improve our process, a novel machine learning technique is utilized to accelerate the calibration effort. The idea is to automate the vibration rating procedure such that given the relevant power-train parameters, a vibration rating associated with that driving condition can be predicted. This process is divided into two (2) prediction models.
Technical Paper

Prediction of Autoignition and Flame Properties for Multicomponent Fuels Using Machine Learning Techniques

2019-04-02
2019-01-1049
Machine learning methods, such as decision trees and deep neural networks, are becoming increasingly important and useful for data analysis in various scientific fields including dynamics and control, signal processing, pattern recognition, fluid mechanics, and chemical synthesis, etc. For future engine design and performance optimization, there is an urgent need for a robust predictive model which could capture the major combustion properties such as autoignition and flame propagation of multicomponent fuels under a wide range of engine operating conditions, without massive experimental measurement or computational efforts. It will be shown that these long-held limitations and challenges related to complex fuel combustion and engine research could be readily solved by implementing machine learning methods.
Technical Paper

Security in Wireless Powertrain Networking through Machine Learning Localization

2019-04-02
2019-01-1046
This paper demonstrates a solution to the security problem for automotive wireless powertrain networking. That is, the security for wireless automotive networking requires a localization function before we allow a node to join the network. We explain why for powertrain wireless networking, this ability of identifying the precise location of a communicating wireless node is critical. In this paper, we explore existing methods that others have used to implement localization for wireless networking. Then, we apply machine-learning techniques to a dataset that has localization information associated with received signal strength indication. We reveal insights provided by our dataset though an exploration with statistics and visualization. We then present our problem in terms of pattern recognition via multiple techniques, including Naïve Bayes Classifier and Artificial Neural Networks.
Technical Paper

Real Time Energy Management of Electrically Turbocharged Engines Based on Model Learning

2019-04-02
2019-01-1056
Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery.
Technical Paper

Backseat Driver - Driver Advisory System

2019-04-02
2019-01-0880
Most V2X, ADAS and autonomous driving systems today are based on the precise location and prediction of movement. These systems are computationally complex and depend on precise sensor measurements. This might not be always possible e.g. inaccurate GPS location during cloudy weather conditions. Proposed here is a new approach, the “Backseat Driver”. The “Backseat Driver” is based on heuristics and machine learning concepts for modeling driving guidance. This is very similar to how “humans” drive. The Backseat Driver complements the existing V2X and RADAR based systems by issuing an advisory to the driver. A machine learning approach (Artificial Neural Networks, Expectation-Maximization, Decision trees) is adapted in order to generate advisories. Moreover, with continuous reinforced learning, the predictions become more accurate.
Technical Paper

“Fitting Data”: A Case Study on Effective Driver Distraction State Classification

2019-04-02
2019-01-0875
The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.
Technical Paper

Study of machine learning algorithms to state of health estimation of iron phosphate lithium-ion battery used in fully electric vehicles

2018-09-03
2018-36-0178
State of Health (SOH) is an important parameter in Battery Management Systems (BMS) because it avoids the failure of a battery that could lead to reduced performance, operational impairment and even catastrophic failure, especially in electric vehicles. However a reliable battery state estimation management system in electric vehicles greatly depends on the validity and generalizability of battery models. This paper presents a generic data-driven approach for lithium-ion battery health management that eliminates the dependency of battery physical models for SOH estimation. In this work, iron phosphate Lithium-ion batteries were used. They were repeatedly submitted to charge-discharge cycles based on standard IEC and ISO profiles. The tension, current, charge, cell temperature and ambient temperature were constantly monitored in this period, and one big data set was created and stored.
Technical Paper

Methodology to Recognize Vehicle Loading Condition - An Indirect Method Using Telematics and Machine Learning

2019-01-09
2019-26-0019
Connected vehicles technology is experiencing a boom across the globe. Vehicle manufacturers have started using telematics devices which leverage mobile connectivity to pool the data. Though the primary purpose of the telematics devices is location tracking, the additional vehicle information gathered through the devices can bring in much more insights about the vehicles and its working condition. Cloud computing is one of the major enabled for connected vehicles and its data-driven solutions. On the other hand, machine learning and data analytics enable a rich customer experience understanding different inferences from the available data. From a fleet owner perspective, the revenue and the maintenance costs are directly related to the usage conditions of the vehicle. Usage information like load condition could help in efficient vehicle planning, drive mode selection and proactive maintenance [1].
Technical Paper

Non-Invasive Real Time Error State Detection for Tractors Using Smart Phone Sensors & Machine Learning

2019-01-09
2019-26-0217
Condition Monitoring is the process of identifying any significant change in operating parameters of a machine, which can be indicative of a failure in future. This paper discuss a non-invasive condition monitoring methodology for sensing and investigating the problems which could be identified by noise and vibrations. This could be an easy solution for predicting failures in tractors which are operational in the field. An example of engine tappet is used to demonstrate the methodology. A disturbed setting causes a distinguishable noise, referred to as “tappet rattle”. Android smartphones (with inbuilt sensors - accelerometer, gyroscope and microphone) are used to record noise and vibration from tractors in good condition as well as in disturbed condition. Time series data analysis is done to extract relevant features and then Fourier Transform is applied to the signals for extracting frequency domain signatures.
Technical Paper

Design and Implementation of Digital Twin for Predicting Failures in Automobiles Using Machine Learning Algorithms

2019-10-11
2019-28-0159
The drastic technological advancements in the field of autonomous vehicles and connected cars lead to substantial progression in the commercial values of automobile industries. However, these advancements force the Original Equipment Manufacturers (OEMs) to shift from feedback-based reactive business analysis to operational-data based predictive analysis thereby enhancing both the customer satisfaction as well as business opportunities. The operational data is nothing but the parameters obtained from several parts of an automobile during its operation such as, temperature in radiator, viscosity of the engine oil and force applied over the brake disk. These operational data are gathered using several sensors implanted in different parts of an automobile and are continuously transmitted to backend computers to develop Digital Twin, which is a virtual model of the physical automobile.
Technical Paper

Fault Detection in Single Stage Helical Planetary Gearbox Using Artificial Neural Networks (ANN) and Decision Tree with Histogram Features

2019-10-11
2019-28-0151
Drive train failures are most common in wind turbines. Lots of effort has been made to improve the reliability of the gearbox but the truth is that these efforts do not provide a lifetime solution. Majority of failures are caused by bearing and gearbox. It also states that wind turbine gearbox failure causes the highest downtime as the repair has to be done at Original Equipment Manufacturer [OEM]. This work aims to predict the failures in planetary gearbox using fault diagnosis technique and machine learning algorithms. In the proposed method the failing parts of the planetary gearbox are monitored with the help of accelerometer sensor mounted on the planetary gearbox casing which will record the vibrations. A prototype has been fabricated as a miniature of single stage planetary gearbox. The vibrations of the healthy gearbox, sun defect, planet defect and ring defect under loaded conditions are obtained. The signals show the performance characteristics of the gearbox condition.
Technical Paper

Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal

2019-10-11
2019-28-0142
In milling process, the quality of the machined component is highly influenced by the condition of the tool. Hence, monitoring the condition of the tool becomes essential. A suitable mechanism needs to be devised in order to monitor the condition of the tool. To achieve this, condition monitoring of milling tool is taken up for the study. In this work, the condition of the tool is classified as good tool and tool with common faults in face milling process such as flank wear, worn out and breakage of the tool based on machine learning approach using statistical feature and decision tree technique. Vibration signals of the milling tool are obtained during machining of mild steel. Statistical features are extracted from the obtained signal, in which the important features are selected using decision tree. The selected features are given as the input to the same algorithm. The output of the algorithm is utilized for classifying the different conditions of the tool.
Technical Paper

Digital Twins for Prognostic Profiling

2019-11-21
2019-28-2456
Ability to have least failures in products on the field with minimum effort from the manufacturers is a major area of focus driven by Industry 4.0 initiatives. Amidst traditional methods of performing system/subsystem level tests often does not enable the complete coverage of a machine health performance predictions. This paper highlights a workable workflow that could be used as a template while considering system design especially employing Digital Twins that help in mimicking real-life scenarios early in the design cycle to increase product’s reliability as well as tend to near zero defects. With currently available disruptive technologies, systems integrated multi-domain 'mechatronics' systems operating in closed-loop/close-interaction. This poses great challenge to system health monitoring as failure of any component can trigger catastrophic system failures.
Technical Paper

Self-Expressive & Self-Healing Closures Hardwares for Autonomous & Shared Mobility

2019-11-21
2019-28-2525
Shared Mobility is changing mobility trends of Automotive Industry and its one of the Disruptions. The current vehicle customer usage and life of components are designed majorly for personal vehicle and with factors that comprehend usage of shared vehicles. The usage pattern for customer differ between personal vehicle, shared vehicle & Taxi. In the era of Autonomous and Shared mobility systems, the customer usage and expectation of vehicle condition on each & every ride of vehicle will be a vehicle in good condition on each ride. The vehicle needs systems that will guide or fix the issues on its own, to improve customer satisfaction. We also need a transformation in customer behavior pattern to use shared mobility vehicle as their personal vehicle to improve the life of vehicle hardwares & reduce warranty cost. We will be focusing on Vehicle Closure hardware & mechanisms as that will be the first and major interaction point for customers in vehicle.
Technical Paper

Technology from Highly Automated Driving to Improve Active Pedestrian Protection Systems

2017-03-28
2017-01-1409
Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.
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